Sirindhorn International Institute of Technology

About: Sirindhorn International Institute of Technology is a based out in . It is known for research contribution in the topics: Supply chain & Combustion. The organization has 1048 authors who have published 1678 publications receiving 30067 citations.

Improving emotion classification in imbalanced YouTube dataset using SMOTE algorithm

Abstract: The imbalanced dataset problem triggers degradation of classification performance in several data mining applications including pattern recognition, text categorization, and information filtering tasks. To improve emotion classification performance, we use a sampling-based algorithm called SMOTE, which oversamples instances in a minority class to the number of those from the majority class. YouTube dataset was balanced using the SMOTE technique and tested using three machine learning algorithms, namely multinomial Naive Bayes (MNB), decision tree (DT) and support vector machines (SVM). As a result, SVM achieves the highest accuracy with 93.30% on filtering task and 89.44% on classification. The SMOTE technique can solve the imbalanced data problem and obtain an improved classification result.

Factors affecting construction performance: exploratory factor analysis

Abstract: The present work attempts to develop a multidimensional performance evaluation framework for a construction company by considering all relevant measures of performance. Based on the previous studies, this study hypothesizes nine key factors, with a total of 57 associated items. The hypothesized factors, with their associated items, are then used to develop questionnaire survey to gather data. The exploratory factor analysis (EFA) was applied to the collected data which gave rise 10 factors with 57 items affecting construction performance. The findings further reveal that the items constituting ten key performance factors (KPIs) namely; 1) Time, 2) Cost, 3) Quality, 4) Safety & Health, 5) Internal Stakeholder, 6) External Stakeholder, 7) Client Satisfaction, 8) Financial Performance, 9) Environment, and 10) Information, Technology & Innovation. The analysis helps to develop multi-dimensional performance evaluation framework for an effective measurement of the construction performance. The 10 key performance factors can be broadly categorized into economic aspect, social aspect, environmental aspect, and technology aspects. It is important to understand a multi-dimension performance evaluation framework by including all key factors affecting the construction performance of a company, so that the management level can effectively plan to implement an effective performance development plan to match with the mission and vision of the company.